A promising approach to this problem of few-shot classification is the family of metric learning techniques, where the standard parametric linear classifier head is replaced with a class-agnostic distance function.
Class membership is determined by distance in latent space from a point or points known to belong to each class.
Simple distance functions such as cosine [1]}, [2]} and Euclidean distance [3]} lead to surprisingly powerful classifiers, though more complex [4]}, non-Euclidean [5]}, and even learned parametric options [6]} are possible, and yield sizable gains.
